Fourth-year CS student at Rutgers finishing a bachelor’s degree and preparing to start a career in software development.
A curated set of class projects and personal work that show the programming languages, tools, and problems I have tackled while completing my degree.
Combined multiple RAG components document loaders, text splitters, vector-embedding models, and a vector database into a single pipeline, constructing a fully functional Q&A chatbot under a frontend Gradio Interface. Leveraged LangChain and LLMs to retrieve and answer natural-language questions grounded in content extracted from more than 30 PDF documents.
Spearheaded backend development, implementing a prompt builder python file that performs basic text processing to create a prompt that writes and returns an SQL query based on real-world home mortgage disclosure act data (HMDA). Engineered a reusable SSH-tunnel and integrated it with the local LLM-Pipeline, enabling several end-to-end submissions of natural-language questions and execution of the appropriate SQL query against a remote Postgres database in the iLab virtual environment.
Fourth-year CS student at Rutgers finishing a bachelor’s degree and preparing to start a career in software development.
I am Varun Iyer, a senior at Rutgers University completing a Bachelor’s in Computer Science. I am finishing my degree and preparing to start my professional career in Data Science/Machine Learning and related fields. I focus on building practical skills and coursework projects as I transition from university to full time roles.
Experience
Data Science Intern
Strategic Tech Solutions · Sep 2025 - Present
Education
Rutgers University 6New Brunswick
Bachelor of Science in Computer Science · Class of 2027
Skills
Combined multiple RAG components document loaders, text splitters, vector-embedding models, and a vector database into a single pipeline, constructing a fully functional Q&A chatbot under a frontend Gradio Interface. Leveraged LangChain and LLMs to retrieve and answer natural-language questions grounded in content extracted from more than 30 PDF documents.
Spearheaded backend development, implementing a prompt builder python file that performs basic text processing to create a prompt that writes and returns an SQL query based on real-world home mortgage disclosure act data (HMDA). Engineered a reusable SSH-tunnel and integrated it with the local LLM-Pipeline, enabling several end-to-end submissions of natural-language questions and execution of the appropriate SQL query against a remote Postgres database in the iLab virtual environment.
Spearheaded development of a concurrent, multi-client chat server using TCP sockets and poll()-based I/O multiplexing, supporting 50+ simultaneous connections with minimal CPU overhead. Coordinated with one other team member to develop a lightweight custom protocol for real-time registration, status updates, and private messaging, with structured error handling achieving 100% uptime under multi-client stress testing.
Developed a Java program using a custom 3-layer hierarchical linked list (state county community) to structure and analyze regional environmental risk data. Synthesized cross-domain datasets (demographics, pollution, flood risk) to identify correlation factors across hundreds of data points. Designed multi-level traversal and insertion algorithms to reduce search complexity for regional data queries.